Explainable Artificial Intelligence for Improved Modeling of Processes

TL;DR

This paper evaluates the use of modern Transformer and classical machine learning models for process modeling in business, demonstrating their predictive capabilities and interpretability through attention mechanisms and feature relevance on benchmark datasets.

Contribution

It introduces the application of Transformer architectures and explainability techniques to process modeling, highlighting their ability to predict outcomes and provide process insights.

Findings

ML models effectively predict process outcomes

Attention mechanisms reveal key process features

Models outperform traditional approaches on benchmarks

Abstract

In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.